Download Factors affecting survival in Mediterranean populations of the

Factors affecting survival in Mediterranean
populations of the Eurasian eagle owl
Mario León-Ortega, María del Mar
Delgado, José E. Martínez, Vincenzo
Penteriani & José F. Calvo
European Journal of Wildlife
Research
ISSN 1612-4642
Volume 62
Number 6
Eur J Wildl Res (2016) 62:643-651
DOI 10.1007/s10344-016-1036-7
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Author's personal copy
Eur J Wildl Res (2016) 62:643–651
DOI 10.1007/s10344-016-1036-7
ORIGINAL ARTICLE
Factors affecting survival in Mediterranean populations
of the Eurasian eagle owl
Mario León-Ortega 1 & María del Mar Delgado 2 & José E. Martínez 1,3 &
Vincenzo Penteriani 2,4 & José F. Calvo 1
Received: 21 March 2016 / Revised: 6 July 2016 / Accepted: 27 July 2016 / Published online: 12 August 2016
# Springer-Verlag Berlin Heidelberg 2016
Abstract The survival rate is a key parameter for population
management and the monitoring of populations. Thus, an
analysis of survival rate variations and the factors influencing
the same is essential for understanding population dynamics.
Here, we study the factors determining the survival and the
causes of mortality of the Eurasian eagle owl (Bubo bubo) in
two Spanish Mediterranean populations (Murcia and Seville)
where the species has a high population density and breeding
success; yet its survival rates and the factors that affect them
are unknown. Between 2003 and 2010, 63 breeding owls were
captured and radio-tracked. Three monthly (quarterly) survival rates were estimated using known-fate models in the program MARK. The mean overall annual survival rate was
0.776 (95 % CI: 0.677, 0.875). We observed survival differences between sexes, and between the breeding and nonbreeding periods, although no overwhelming support was
found for any particular model. We concluded that (i) females
have a lower survival rate than males, probably due to their
larger home ranges, which increase the risk of mortality; (ii)
the survival rates of both sexes were lower during the nonbreeding period; and (iii) the causes of mortality differed
* Mario León-Ortega
[email protected]
1
Departamento de Ecología e Hidrología, Universidad de Murcia,
Campus de Espinardo, Murcia, Spain
2
Research Unit of Biodiversity (UMIB, UO-CSIC-PA), Oviedo
University - Campus Mieres, 33600 Mieres, Spain
3
Bonelli’s Eagle Study and Conservation Group, Apdo. 4009,
E-30080 Murcia, Spain
4
Department of Conservation Biology, Estación Biológica de Doñana,
C.S.I.C., c ⁄ Americo Vespucio s ⁄ n, 41092 Sevilla, Spain
significantly between the two populations, gunshot being the
main cause in Seville and electrocution in Murcia.
Keywords Home range . Human-induced mortality .
Known-fate model . Sex-biased mortality
Introduction
The study of demographic parameters is essential for understanding the trends and changes of wild populations (Moyes
et al. 2006; Schaub et al. 2010; Smith et al. 2010; Tenan et al.
2012). Among various vital rates that affect population change
(i.e. survival, productivity, immigration/emigration), survival
is usually considered the most important driver of population
dynamics, because this parameter is usually the greatest contributor to the population growth rate in K-selected organisms
(Sibly and Hone 2002; Grande et al. 2009; Margalida et al.
2014). Thus, annual survival can be a key parameter for monitoring and managing populations of conservation concern,
especially in the case of long-lived species (Wisdom et al.
2000; Hernández-Matías et al. 2011). Thus, understanding
patterns of variation in survival is necessary to interpret both
life history variation and the mechanisms driving population
change (Robinson et al. 2010; Smith et al. 2014).
To our knowledge, although few estimates on owl survival
in Mediterranean European regions have been published (e.g.
Boano and Silvano 2015), owl survival estimates are available
for several species in boreal and temperate regions of Europe
and North America (Newton et al. 2016). These studies revealed that owl survival (i) shows temporal patterns of variation related to cyclic food abundance and climatic factors
(Seamans et al. 2002; Francis and Saurola 2004; Lehikoinen
et al. 2011; Pavón-Jordán et al. 2013; Dugger et al. 2016); (ii)
is influenced by habitat structure (Franklin et al. 2000;
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Hakkarainen et al. 2008; Dugger et al. 2005); (iii) decreases
with the presence of human infrastructures (Grilo et al. 2012;
Thorup et al. 2013; Borda de Água et al. 2014); and (iv) is
reduced by the predation of young individuals (Sunde 2005).
In addition to age, other individual-related variables, such as
sex or physical condition, are expected to be important factors
affecting survival. For example, age- and sex-biased mortality
has been observed in many wild bird populations (Tavecchia
et al. 2001; Martín et al. 2007; Rymešová et al. 2012), and
other studies have documented higher survival rates for individuals with better body condition or health status (Dinsmore
and Collazo 2003; Hylton et al. 2006). Differences in survival
rates between males and females are usually associated to
differences in behaviour and energetic investment during the
reproduction period (Liker and Székely 2005; Rymešová et al.
2012), but also to differences in habitat use or foraging activities (Lambertucci et al. 2012).
The Eurasian eagle owl Bubo bubo (Linnaeus, 1758) is
socially monogamous, long-lived (>15 years in the field
and >60 years in captivity; Penteriani et al. 2010), nonmigratory and territorial, life history traits that would predict high adult survival rates. Previous research suggests
that Eurasian eagle owls may experience high mortality
by electrocution and direct persecution from humans
(Marchesi et al. 2002; Sergio et al. 2004; Martínez et al.
2006; Rubolini et al. 2001), and two studies suggest that
survival rates increase with age (Olsson 1997; Schaub
et al. 2010), although other possible factors such as sex
were not taken into account.
Survival is frequently estimated using capture-markrecapture techniques based on recaptures and/or recoveries
of birds ringed with metal or coloured rings, or a combination
of both (Lebreton et al. 1992), or by radio-tracking (Kenward
2001). These methods have been widely used in population
monitoring studies, which determine general demographic parameters in wildlife, such as immigration, emigration, survival
and population size (Altwegg et al. 2003, Newton et al. 2016).
In particular, the use of radio telemetry represents an ideal
method for survival studies, as it provides unbiased results
for relatively long periods of an individual’s life (Smith et al.
2010; Thorup et al. 2013, Newton et al. 2016).
In this paper, we aim to assess the factors associated with
variation in survival and attempt to identify the causes of death
in two Spanish populations of the Eurasian eagle owl, using
radio-tracking methods and known-fate survival models. The
two populations studied are located in areas characterised by
the high availability of rabbits Oryctolagus cuniculus (the
main prey of the Eurasian eagle owl; Penteriani et al. 2010)
and different degrees of humanization (so that habitat management and land uses may be determining differences in the
survival rates). This species reaches high densities in our study
areas which allowed us to capture a large number of territorial
individuals with relative ease by trapping (Campioni et al.
Eur J Wildl Res (2016) 62:643–651
2013). The objectives of this study were (1) to determine
temporal patterns of survival, (2) to examine the effects of
sex and age on the survival rates of Eurasian eagle owls, (3)
to compare survival rates between the two populations, living
in areas characterised by different human pressures and environmental characteristics and (4) to document the causes of
mortality and analyse the factors influencing it.
Methods
Study areas
The study was carried out from January 2003 to December
2010 in two populations in southern Spain: (i) the Sierras of
Columbares, Altaona and Escalona, a hilly area ranging from
40 to 646 m a.s.l. in the province of Murcia (south-eastern
Spain 37°45′ N, 0°57′ W; hereafter Murcia), which includes
a special protection area (SPA) ‘Monte El Valle y Sierras de
Altaona y Escalona’ (ES0000269; León-Ortega et al. 2014);
and (ii) the Sierra Norte of Seville (Sierra Morena massif,
south-western Spain; 37°30′ N, 06°03′ W; hereafter Seville),
a hilly area 60–200 m a.s.l. (Penteriani et al. 2005).
Both study areas share a Mediterranean climate and have
high densities of rabbits and Eurasian eagle owl pairs (ca. 40
territories per 100 km2 over the whole study area; Penteriani
et al. 2010; León-Ortega et al. 2014). The landscape in Murcia
is a mosaic of forest of Aleppo Pines Pinus halepensis, scrublands, irrigated and rainfed crops, hunting enclosures and
urbanised areas. In contrast, the Seville landscape is dominated by sparse woodlands composed of Holm Oaks Quercus
ilex, Gall Oaks Quercus faginea, Stone Pine Pinus pinea,
Olive Trees Olea europaea, Mastic Tree Pistacea lentiscus
and small plantations of Eucalyptus sideroxylon. In many
areas, scrubland has replaced woodland. Most of the area is
managed for game species (mainly partridges and rabbits).
Trapping and radio-tracking
Owls were trapped using two methods: (i) simulation of a
territorial intrusion using a combination of a taxidermy mount
of an Eurasian eagle owl and a mist-net to capture the territorial bird when it responds to the simulated intruder (Penteriani
et al. 2007); (ii) placement of a bow-net in the nest when
nestlings were 20–35 days old (i.e. when they were already
able to thermoregulate; Campioni et al. 2013) so that adults
could be captured when they returned to care for chicks at the
nest. Each captured bird was fitted with a 30 g radiotransmitter (Biotrack Ltd, Wareham, Dorset, UK) attached as
a backpack harness made from Teflon ribbon, which
contained a mercury posture sensor that allowed us to discriminate whether the owls were alive or dead. Battery life was
almost more than 1 year for each individual, so between 2003
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and 2010, we monitored radioed individuals until they died or
stopped transmitting. We recorded no adverse effect of backpacks on birds or breeding performance. The backpacks were
not removed after the study due to the difficulty of recapturing
the same individual (Penteriani et al. 2011). To locate the
owls, the surveys were made from four-wheel drive vehicles,
using three-element hand-held Yagi antennas (Biotrack;
Wareham, Dorset, UK; http://www.biotrack.co.uk) with
either Stabo (XR-100) portable ICOM receivers (IC-R20) or
SIKA radio tracking receivers. We did at least two attempts
per month to locate each of the radio-tagged individual. When
an owl was detected, we proceeded to determine its status
(alive, dead). Birds not detected in successive occasions were
considered censored (i.e. end of monitoring).
Survival analysis
To estimate survival and determine factors affecting owl survival, we used the know-fate model with the logit-link function in the program MARK (Pollock et al. 1989, White and
Burnham 1999). Know-fate modelling is an appropriate method for estimating survival parameters in radio-tracking studies, in which the status (dead or alive) of all tagged animals is
known at each sampling occasion. We estimated survival rates
for 32 3-month (quarterly) intervals, using the 63 encounter
histories obtained by relocating the radio-tracked individuals.
The individual covariables included sex, age, population
(Murcia, Seville) and two time-specific factors (year and period). To test which variables were most likely to influence
owl survival, a set of candidate models was specified, including a number of additive and interaction models representing
plausible biological hypotheses.
Birds were sexed by molecular procedures using DNA extracted from blood (Griffiths et al. 1998) or using discriminant
functions based on body measurements that classified 98.4 %
of the birds correctly (Delgado and Penteriani 2004). Eurasian
eagle owls can begin to breed in the second calendar year, and
the bird age can be assessed by plumage characteristics until
the fifth year (Martínez et al. 2002). Therefore, we classified
the individuals into two age-class groups: juvenile (owls from
1 to 5 years old) and adult (owls older than 5 years).
To examine inter- and intra-annual variations in survival,
we considered two time-specific group covariables: year and
period. Inter-annual variations were studied using 8 years
(2003–2010), and the annual cycle was divided into breeding
and non-breeding periods, each related with different breeding
stress, home range activity and food availability. The breeding
season includes 9 months (three quarterly intervals), from
December to August, and the non-breeding season includes
only 3 months (one quarterly interval), from September to
November (Campioni et al. 2013).
Model selection was performed using Akaike’s information
criterion corrected for small sample size (AICc) and models
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differing by ≤2 ΔAICc were considered as potential alternatives
(Burnham and Anderson 2002). Akaike weights (wi) were used
to evaluate the strength of evidence among competing models.
The degree to which 95 % confidence intervals for covariate
coefficients (βi) overlapped zero was also used to evaluate the
strength of evidence for competing models.
Because there is no goodness-of-fit for classical
known-fate data, the variance inflation factor (i.e. the
overdispersion term ĉ) cannot be estimated adequately.
Therefore, model robustness was investigated following
the approach described in Smith et al. (2014), i.e. artificially inflating the overdispersion term from 1 to 3 (i.e. no
dispersion to extreme dispersion) to simulate various
levels of dispersion reflected in Quasi-AICc (QAICc).
We used the model-averaging procedure in program
MARK to estimate survival rates. The 95 % confidence intervals (CI) for annual rates were calculated using the deltha
method (Cooch and White 2016).
Causes of mortality
The cause of mortality was determined through necropsy,
although it could be identified visually in most cases. We
classified the causes of mortality into the following
classes:
(i) Human-induced causes. (1) Gunshot: pellets observed
through radiography, or when the holes were clearly visible in the body or wings. (2) Electrocution: the carcass
was found below power lines, usually with burns in both
plumage and claws caused by electrocution. (3) Collision
with fences: attributed to carcasses found close to fences
and with broken bones or neck.
(ii) Natural causes. (1) Killing by other Eurasian eagle owls:
the carcass was found partly eaten under roots, perches
or in the nests of other territorial owls. (2) Disease: the
carcass was found shortly after death and was complete,
with no sign of injury through predation, starvation or
human manipulation, but the necropsy showed abnormalities in internal organs.
(iii) Unknown: the carcass was found without any external
evidence and a high degree of decomposition that did
not permit us to determinate the cause of death.
In order to investigate the factors influencing the
causes of mortality, and given the low number of dead
owls found, the cause of mortality was modelled as a
nominal response variable with only four classes: gunshot, electrocution, disease and other. We then performed
a multinomial regression analysis (Venables and Ripley
2002) and type II likelihood-ratio tests (Fox and
Weisberg 2011) to examine the significance of three potential explanatory variables: sex, population and period.
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Multinomial regression is a simple extension of binary
logistic regression that allows for more than two categories of the dependent variable and is considered suitable
for analysing multiway contingency tables. The analyses
were performed using the ‘nnet’ (Venables and Ripley
2002) and ‘car’ (Fox and Weisberg 2011) packages in R
version 3.1.1 (R Core Team 2014).
Eur J Wildl Res (2016) 62:643–651
Table 1 Summary of the radio-tracking data of Eurasian eagle owls in
Murcia and Seville, 2003–2010
Murcia
Females
Seville
Overall
15
12
27
Adult
4
0
4
Immature
Males
11
15
12
21
23
36
Adult
7
0
7
Results
Immature
Total adult
8
12
21
0
29
12
Between 2003 and 2010, we captured radio-tagged and
tracked 63 breeding individuals (Table 1), of which 27 were
females and 36 males. Most of them (51) were aged as juvenile (≤5 years old) and 12 were aged as adults.
Of the 24 candidate models selected to examine the influence of the variables considered on survival, the additive model including sex and period (breeding vs. non-breeding season) was the most parsimonious (Table 2), although the model
including only sex, and the interaction term between sex and
period, was competitive. The remaining models were >2.00
ΔAICc, providing little support for the effects of age, population or year on survival rates. The best model provided support
for higher survival rates for males and the breeding season
compared to females and the non-breeding season (Fig. 1).
These differences were not very large (less than 0.1), although
they appear more important when expressed as annual rates
0.842 (95 % CI 0.738, 0.947) for males vs. 0.667 (95 % CI
0.492, 0.842) for females. Nevertheless, the low Akaike
weights of the best alternative models indicate no overwhelming support for any particular model, although the slope coefficients for period and the sex × period interaction had 95 %
confidence intervals that included zero (Table 2), suggesting
weak differences between periods in survival rates.
Furthermore, when we set ĉ = 2, the constant (null) model
was a plausible alternative model (ΔQAICc = 0.93), and when
ĉ = 3, the constant model was the best model, which points to
no substantial variation in survival attributable to the factors
analysed. The quarterly estimate of survival of the constant
model was 0.939 (95 % CI 0.910, 0.959), which yields a mean
overall annual survival rate of 0.776 (95 % CI 0.677, 0.875).
A total of 24 deaths (38.1 %) were recorded during the
study among the radio-tracked individuals. The main causes
were human-induced (Table 3). Gunshot and electrocution
represented 37.5 and 29.2 % of the deaths, respectively.
Death by disease also showed a high percentage (16.7 %)
of deaths. The multinomial analysis did not show any difference in the cause of mortality between sexes (χ2 = 2.35,
df = 3, P = 0.503), or between breeding and non-breeding
seasons (χ2 = 0.99, df = 3, P = 0.804). However, differences
were observed among populations (χ2 = 11.88, df = 3, P =
0.008), with gunshot being the first cause for Seville and
electrocution for Murcia.
Total immature
Total individuals tracked
18
30
33
33
51
63
Death
12
12
24
Censored
5
21
26
Alive at the end of the study
Average tracking time (months)
13
16.5
0
20.5
13
18.6
Adult owls are individuals aged >5 years old
Discussion
Our results obtained using known-fate analysis with
radio-tracked breeders identified a sex-biased survival rate
in favour of males, and differences between the breeding
and non-breeding periods of the annual cycle. Sex-biased
survival has commonly been reported in demographic
studies of bird populations (Donald 2007), and some studies demonstrate survival differences between different
phases of the biological cycle (e.g. Robinson et al.
2010; Leyrer et al. 2013; Varmer et al. 2014).
This work provides evidence of different survival rates
between genders, the annual survival rate being 1.26
times higher for males. Some studies have suggested that
differences in male and female mortality are positively
correlated with size dimorphism (Tavecchia et al. 2001)
or asymmetric habitat use by the two sexes (Lambertucci
et al. 2012). These differences in sex survival are sometimes associated with anthropogenic causes in large birds
of prey (Ferrer and Hiraldo 1992), which affects the adult
sex ratio of wild populations and causes males to outnumber females, even when the offspring sex ratio is 1:1 (as in
the Eurasian eagle owl case; Mora et al. 2010). In fact,
higher female mortality, rather than a skewed offspring
sex ratio, is the main driver of male-skewed adult sex
ratios in bird populations (Donald 2007; Székely et al.
2014), a circumstance which may influence multiple aspects of pair-bond and mating behaviour (Liker et al.
2014), and may have important consequences for the viability of populations of long-lived organisms that appear
numerically stable (Grayson et al. 2014).
Because no differences in the proportion of mortalities attributed to each cause by sex were observed, the higher
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Table 2 Summary of known-fate
survival model selection results
for Eurasian eagle owls in Murcia
and Seville, 2003–2013 when the
overdispersion term was 1.0.
Models are ranked according to
Akaike’s information criterion
(AICc)
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Model
K
AICc
ΔAICc
wi
Coefficients (95 % CI)
Ssex+period
3
178.18
0.00
0.28
βsex = 1.048 (0.204, 1.892)
Ssex
Ssex*period
2
4
178.57
179.60
0.39
1.41
0.23
0.14
βperiod = 0.713 (−0.158, 1.584)
βsex = 1.034 (0.194, 1.874)
βsex = 1.513 (0.052, 2.973)
βperiod = 1.016 (−0.125, 2.162)
βsex×period =−0.718 (−2.513, 1.078)
Ssex+population
Ssex+age
Speriod
Sconstant
Ssex*age
Spopulation+period
Spopulation
Sage*period
Sage+period
Sage
Sage+population
Ssex+year
Syear
Spopulation+year
Sage+year
3
3
2
1
4
3
2
4
3
2
3
9
180.46
180.58
182.17
182.46
182.56
183.69
183.85
183.87
184.11
184.42
185.31
189.49
2.28
2.40
3.99
4.27
4.38
5.51
5.66
5.69
5.93
6.24
7.12
11.30
0.09
0.08
0.04
0.03
0.03
0.02
0.02
0.02
0.01
0.01
0.01
0.00
8
9
9
192.33
194.10
194.28
14.15
15.92
16.10
0.00
0.00
0.00
The symbol ‘+’denotes an additive model, while ‘*’ is used for interaction models. Headers for columns are
number of parameters (K), change in AICc relative to the highest ranked model (ΔAICc) and Akaike weight (wi).
Covariate coefficients and 95 % confidence intervals (CI) are given only for the best (ΔAICc < 2.0) models
mortality of females is probably associated with differences in
behaviour, habitat use or energetic investment during the reproduction period. More specifically, we hypothesise that the
higher mortality of female Eurasian eagle owls is associated
with differences in habitat use by the two sexes, with females
foraging in larger home ranges (Campioni et al. 2013), which
increase the interaction with human infrastructures such as
Fig. 1 Three monthly (quarterly) survival probabilities of Eurasian eagle
owls estimated using model-averaging in known-fate analyses, Table 2.
Vertical lines represent 95 % confidence intervals
power lines, fences, roads and hunting enclosures. In addition,
survival may be associated to the different home range behaviour (e.g. space use, movement patterns and rhythms of activity)
of males and females during the different phases of the biological cycle (Campioni et al. 2013). Our results point to survival
differences between the breeding and non-breeding periods, but
we observed lower quarterly survival rates during the nonbreeding period, whereas, given the reproductive effort and
the degree of stress of individuals during the breeding season,
the opposite pattern might be expected (Severinghaus and
Rothery 2001; Thorup et al. 2013). During the breeding season,
Eurasian eagle owl pairs spend most time in or near the nest, a
behaviour characteristic that could reduce the exposure time in
the most humanised areas of their home ranges, thereby
minimising the risk of being shot or electrocuted. In addition,
with respect to food availability in these two periods, the
marked intra-annual variations in the density of rabbits could
affect survival rates between seasons. Rabbits are less abundant
in late summer and autumn (the non-breeding season for the
owls), due to a hunting pressure, the higher frequency of disease
and limited food availability because of summer droughts
(Beltrán 1991). Hence, the intra-annual variation in the density
of prey could be associated with the intra-annual pattern of
survival in the Eurasian eagle owl. Variation in annual survival
rates in many owl species in northern latitudes has been shown
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Eur J Wildl Res (2016) 62:643–651
Table 3 Causes of death of
Eurasian eagle-owls in the study
areas, 2003–2010
Murcia
Cause
Seville
Overall
Male
Female
Male
Female
Male
Female
Total
Gunshot
0
1
5
3
5
4
9
Electrocution
2
4
0
1
2
5
7
1
0
0
0
1
0
1
Killing
Disease
0
1
0
3
1
0
0
0
1
1
0
3
1
4
Unknown
0
0
0
2
0
2
2
Total dead
4
8
6
6
10
14
24
Human-induced
Collision with fences
Natural
to vary annually (Altwegg et al. 2003; Francis and Saurola
2004; Le Gouar et al. 2011; Pavón-Jordán et al. 2013) and in
association to the cyclic food abundance of the main prey
(Korpimäki 1992; Rutz and Bijlsma 2006; Karell et al. 2009;
Lehikoinen et al. 2011). However, in Mediterranean ecosystems, predator communities are mostly made up of generalists,
and therefore, predator-prey systems are more complex than
those of northern latitudes (Fargallo et al. 2009; Moleón et al.
2012). The rabbit is a keystone species in southern Europe
(Delibes-Mateos et al. 2007) that does not suffer regular cyclic
oscillations, and, while this prey forms the bulk of the Eurasian
eagle owl diet (Penteriani et al. 2002; Lourenço 2006), the
species has a high capacity to switch to alternative prey if the
basic prey does become scarce (Martínez and Calvo 2001).
This could explain why we found no inter-annual variations
in Eurasian eagle owl survival.
The survival of Eurasian eagle owls in Alpine and
northern latitudes has previously been estimated from
the recovery of owls marked as nestlings (Olsson 1997)
and using a Bayesian-integrated population model combining different data sets (Schaub et al. 2010). In contrast
to our results (models including age were not well supported), these studies pointed to a variation in survival
that depended on age-class, and similar results have been
found in other long-lived species (e.g. Daunt et al. 2007;
Angelier et al. 2007; Grande et al. 2009; HernándezMatías et al. 2011). However, it must be noted that,
unlike the above-mentioned studies, our study has been
conducted on breeding individuals, which may explain
the absence of an age effect on survival. In our study
areas, a substantial proportion of first-time breeders was
being recruited each year, resulting in a high number of
juvenile breeders being captured. This circumstance denotes the existence of a high turn-over rate in territorial
individuals, mainly females, and, although no evidence
of lower survival in juvenile breeders was found, the
differences in breeding success between juvenile and
adult individuals might be worth investigating.
However, assessing the effects of the cost of first reproduction requires a long-term data set with a large number
of individuals (Tavecchia et al. 2001). It should be noted
that the overall survival rate observed in our populations
was intermediate between those reported in the studies of
Olsson (1997) and Schaub et al. (2010).
Of note was the fact that we observed no differences in
survival rates between the two populations studied
(Murcia and Seville), but the primary causes of mortality
were different, but both associated with anthropogenic
affects. In Northern Europe, electrocution and traffic have
been identified as the main sources of Eurasian eagle owl
mortality (Valkama et al. 2015). In Spain, the primary
cause of death in this species is electrocution, followed
by persecution and collisions with game fences and cars
(Martínez et al. 2006). This noted that some humaninduced causes of mortality varied by geographic region.
In our study, the Seville population showed a higher frequency of deaths by shooting, whereas in Murcia, the
most common anthropogenic cause of death was electrocution. These differences appear to be directly related to
range management and land uses in these separate study
areas. The Murcia population inhabits more humanised
landscapes than the Seville population. In Murcia, the
area dedicated to irrigated crops and urbanizations has
grown continuously in the last decades (MartínezFernández et al. 2000, 2013), accompanied by an increase
in other man-made structures, including roads, railways
and new power generation facilities. This could explain
the significant differences found in the causes of mortality
between both populations. Our results suggest that
human-induced mortality may be a pending problem for
Eurasian eagle owl conservation in Spain (Martínez et al.
2006), despite the considerable effort put into the creation
of protected areas during the last 10 years, the identification of dangerous pylons and the application of appropriate insulation techniques on power lines (Moleón et al.
2007; López-López et al. 2011; Barrientos et al. 2012).
Author's personal copy
Eur J Wildl Res (2016) 62:643–651
Acknowledgments We are very grateful to Eloy Peréz, José Alfonso
Lacalle, Letizia Campioni, Chiara Bettega, Rui Lourenço and other collaborators for their help on the field work. We thank Encarni Montoya,
María Victoria Jiménez-Franco and Silvia Villaverde for their unconditional support and comments. The suggestions and comments of Jari
Valkama and two anonymous reviewers considerably helped to improve
the manuscript. The Asociación de Naturalistas del Sureste (ANSE) is
also acknowledged for partial financial support in the Murcia area. The
Spanish Ministry of Economy and Competitiveness (CGL2012–33240;
FEDER co-financing) funded this study in the Seville area. The research
adheres to the legal requirements of the country where it has been carried
out.
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